Machine learning systems and methods for processing data for healthcare applications

ABSTRACT

A method of predicting an outcome of a prior-authorization, claim, or appeal includes receiving, at a server, a natural language data file representing doctors notes from a provider visit related to a service instance; receiving, at the server, a structured data set including patient profile data, diagnosis and procedure codes, and quantitative data related to a payment requested; processing at least the natural language data file using a medical dictionary to output a set of key medical terms; processing, using a supervised machine learning algorithm, the structured data set and the set of key medical terms to predict an outcome of the payment requested; and outputting an indication of the predicted outcome of the payment requested.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of, and priority to, U.S.Provisional Patent Application No. 62/685,678, filed on Jun. 15, 2018,which is hereby incorporated by reference herein in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to machine learning algorithmsand methods for processing structured and natural language data tooutput likelihoods of success and automatically populate lettertemplates.

BACKGROUND

Data sources in the healthcare industry are generally output and storedby a variety of systems and in a variety of formats. For instance,before or after a healthcare provider (e.g., a hospital, a physician'soffice, etc.) provides a service to a patient, the provider oftensubmits an insurance claim to a payer (e.g., the patient's healthinsurance company) for payment for the service(s). In some cases, theinsurance claim is denied by the payer or the payer only pays a portionof the billed amount (often referred to as an adjustment). The providermay appeal the denial or adjustment of the insurance claim to the payeror, in some cases, a third party (e.g., an arbitrator).

SUMMARY

The insurance claim or appeal may contain data from a variety ofsources, including data from the electronic health records “EHR” system(e.g. doctor notes, diagnosis and service codes, test results, etc.),and data from the provider's revenue cycle management (“RCM”) thatincludes medical and billing software, to track patient care episodesfrom registration to appointment scheduling and payment. The providerssubmit this information as prior-authorizations, claims and appeals in avariety of different electronic and paper formats. For instance, somepayers allow these forms to be submitted electronically, but most do notand require paper submission. The formats may include structured datafields including the procedure and diagnosis codes (e.g., CPT codes),data from .pdf or other document types in the form of letters regardingthe claims or appeals, natural language data in the form of textrepresenting doctor's notes, numerical fields relating the billing andpayment history, profile information of payments as structured data andothers.

Once this data is submitted to payers, the payers' systems utilize theirown algorithms to process the data and determine an outcome which isusually an acceptance, denial or partial acceptance of payment. Thepayers then submit their decision in a variety of forms back to theprovider, including by letter, through a data portal, and others. Thedecision may also include data in a variety of forms including textbased data from prose relating to a letter, data output from a payerportal in text delimited form, or other formats.

Based on this data submitted by providers, payers generally utilizealgorithms for accepting or denying prior-authorizations, claims, orappeals, and those algorithms are largely unknown outside the payers,and therefore hard to predict beyond basic threshold rules that includenumber of service visits for a type of service (e.g. six physicaltherapy sessions for back pain). This introduces tremendousinefficiencies into the system, including back and form with theprovider and payer data sending, etc. Therefore, machine learningalgorithms have been developed based on the data sources available thatcan automatically train and input data into the algorithms to predictthe outcomes of the payers' decisions, based on the documents and otherdata available. Additionally, systems and method have been developed toautomatically populate data fields submitted to providers, includingletter templates based on structured and natural language data.

It would be advantageous for providers to have a predictor of claimsuccess prior to filing a claim, and additionally a predictor forpotential reasons for claim denial, in order to determine whether tofile the claim, make edits prior to filing the claim, and which editsshould be made to the claim. It would be advantageous for providers tohave a system suggest further action(s) (in real-time, in someimplementations) by the provider, or provider billing team, in order toincrease likelihood of claim success. It would be advantageous forproviders to better understand clinical diagnosis patterns by beinggiven predictive outcomes based on historical data. In the event a claimis denied by a payer, it would be advantageous for the provider toprioritize a plurality of denied insurance claims for appeal to one ormore payers based on the predicted outcome of the appeal and/or thepredicted value of the appeal. It would be advantageous for providers toobtain real-time, filterable analytics (in some implementations,automated notifications) based on data sources including providersoutside of their own medical group, hospital, or health system. It wouldfurther be advantageous to provide a method of populating an appealdocument, or any element of an entire appeal package, for submission toa payer. It would further be advantageous for providers to utilize dataanalysis to help settle or auto-settle claims with any third-party. Thepresent disclosure is directed to solving these and other problems.

All of the above advantages, and more, can be applied to aid any partywithin the healthcare system, including payers and patients. Further,the above data outputs can also be used by all parties to make moreinformed, faster insurance plan coverage changes. For example, if a newpatient treatment is showing positive testing and clinical signs, yetclaims data shows a high denial rate, adjustments can be made toinsurance coverage. A further example, claims data analysis can becombined with larger clinical data sets to best determine the optimalinsurance coverage plan to support population health and economicfeasibility.

The output of the algorithms described herein provide at least a portionof a foundation for unique cloud-based SaaS applications. Theseapplications can be used to serve unmet needs for many parties involvedin healthcare, including but not limited to, providers, payers, andpatients. More importantly, the US and global healthcare systems wouldbenefit in the areas of efficiency, scalability, flexibility, andtransparency.

According to some implementations of the present disclosure, a method ofprioritizing denied insurance claims for appeal to one or more payerscomprises training a first machine learning algorithm to determine apercentage likelihood that an appeal of an insurance claim to a firstpayer will result in a paid insurance claim; training a second machinelearning algorithm to determine a percentage likelihood that an appealof an insurance claim to a second payer will result in a paid insuranceclaim; receiving a first set of data associated with a first insuranceclaim denied by the first payer; receiving a second set of dataassociated with a second insurance claim denied by the second payer;inputting at least a portion of the first set of data into the firstmachine learning algorithm; using the first machine learning algorithm,determining a first percentage likelihood that an appeal of the firstinsurance claim to the first payer will result in a first paid claim;inputting at least a portion of the second set of data into the secondmachine learning algorithm; and using the second machine learningalgorithm, determining a second percentage likelihood that an appeal ofthe second insurance claim to the second payer will result in a secondpaid claim.

According to some implementations of the present disclosure, a method ofprioritizing denied insurance claims for appeal to one or more payerscomprises training a first machine learning algorithm to predictoutcomes of insurance claim appeals to a first payer; training a secondmachine learning algorithm to predict outcomes of insurance claimappeals to a second payer different from the first payer; receiving afirst set of data associated with a first insurance claim denied by thefirst payer; receiving a second set of data associated with a secondinsurance claim denied by the second payer; inputting at least a portionof the first set of data into the first machine learning algorithm;using the first machine learning algorithm, predicting an outcome of anappeal of the first insurance claim to the first payer; inputting atleast a portion of the second set of data into the second machinelearning algorithm; using the second machine learning algorithm,predicting an outcome of an appeal of the second insurance claim to thesecond payer; and displaying the predicted outcomes on a display device.

According to some implementations of the present disclosure, a method ofprioritizing denied insurance claims for appeal to one or more payerscomprises training a plurality of machine learning algorithms to predictoutcomes of insurance claim appeals, each of the plurality of machinelearning algorithms being associated with a respective one of aplurality of payers; receiving a plurality of sets of data, each of theplurality of sets of data being associated with a respective insuranceclaim denied by one of the plurality of payers; for each of the receivedplurality of sets of data, inputting at least a portion of the receivedplurality of sets of data into a respective one of the plurality ofmachine learning algorithms that is associated with a respective one ofthe plurality of payers and, using the respective one of the pluralityof machine learning algorithms, predicting an outcome of an appeal tothe respective one of the plurality of payers; and displaying thepredicted outcomes on a display device.

According to some implementations of the present disclosure, a method ofprioritizing denied insurance claims for appeal to one or more payerscomprises training a plurality of machine learning algorithms to predictoutcomes of insurance claim appeals, each of the plurality of machinelearning algorithms being associated with a respective one of aplurality of payers; receiving a set of data associated with a pluralityof denied insurance claims, each of the denied insurance claims beingdenied by one of the plurality of payers; dividing the set of data intogroups of data, each of the groups of data being associated withinsurance claims denied by a respective one of the plurality of payers;for each of the groups of data associated with insurance claims deniedby a respective one of the plurality of payers, inputting at least aportion of the received set of data into a respective one of theplurality of machine learning algorithms that is associated with arespective one of the plurality of payers; using the respective one ofthe plurality of machine learning algorithms, predicting an outcome ofan appeal to the respective one of the plurality of payers; grouping theplurality of denied insurance claims into a first group associated witha predicted approved outcome and into a second group associated with apredicted denial outcome; and displaying at least the first groupassociated with a predicted approved outcome on a display device.

According to some implementations of the present disclosure, a method ofpopulating an appeal document for submission to a payer comprisesreceiving a set of data associated with an insurance claim denied by apayer; selecting, based in part on the received set of data, a machinelearning algorithm associated with the payer from a plurality of machinelearning algorithms, each of the plurality of machine learningalgorithms being associated with a respective payer; inputting at leasta portion of the received set of data into the selected machine learningalgorithm associated with the payer; based at least in part on theinputted set of data, populating a plurality of fields of the appealdocument with appeal data for submission to the payer to appeal thedenied insurance claim.

According to some implementations of the present disclosure a method ofpopulating an appeal document for submission to a third party comprisesanalyzing a set of documents to determine a master set of key words andphrases used in the set of documents, the set of documents includinginsurance claim forms prepared by one or more providers, insurance claimappeal forms prepared by one or more providers, doctor notes associatedwith one or more patients, explanation of benefit forms prepared by oneor more payers, claim denial letters prepared by one or more payers,claim acceptance letters prepared by one or more payers, or anycombination thereof, the determining the master set of key words andphrases being based at least in part on (i) words contained in a medicallibrary, (ii) words contained in documents associated with approvedmedical claims, (iii) or a combination of (i) and (ii); training a firstpayer machine learning algorithm to (i) predict outcomes of insuranceclaim appeals submitted to a first payer and (ii) identify a first payerset of key words and phrases to be used in populating an appeal documentto be sent to the first payer, the training including providing thefirst payer machine learning algorithm with a first plurality of subsetsof the master set of key words and phrases and a first plurality ofcorresponding outcomes; training a second payer machine learningalgorithm to (i) predict outcomes of insurance claim appeals submittedto a second payer and (ii) identify a second payer set of key words andphrases to be used in populating an appeal document to be sent to thesecond payer, the training including providing the second payer machinelearning algorithm with a second plurality of subsets of the master setof key words and phrases and a second plurality of correspondingoutcomes, the second plurality of subsets of the master set of key wordsand phrases being different than the first plurality of subsets of themaster set of key words and phrases; receiving a first set of dataassociated with a first denied insurance claim, the first set of dataincluding a first payer name, a first payer address, a first payer phonenumber, doctor notes associated with a first patient, a first patientname, a first patient address, a first patient phone number, a firstclaim number, a first insurance group number, a first plan number, afirst services provided date, a first claim denial date, a firstprocedure code, a first diagnosis code, a first billed amount, a firstallowed amount, a first deductible amount, a first copay amount, a firstadjustment code, a first payment amount, a first appeal deadline date, afirst provider name, a first provider address, a first provider phonenumber, or any combination thereof; selecting, based in part on thereceived first set of data, the trained first payer machine learningalgorithm from a plurality of trained machine learning algorithms;inputting at least a portion of the received first set of data into thefirst payer machine learning algorithm; based at least in part on theinputted first set of data, populating a plurality of fields of anappeal document with appeal data for use in appealing the first deniedinsurance claim to the first payer, the appeal data including selectportions of the doctor notes associated with the first patient, thefirst payer name, the first payer address, the first payer phone number,the first patient name, the first patient address, the first patientphone number, the first claim number, the first insurance group number,the first plan number, the first services provided date, the first claimdenial date, the first procedure code, the first diagnosis code, thefirst billed amount, the first allowed amount, the first deductibleamount, the first co-pay amount, the first adjustment code, the firstpayment amount, the first appeal deadline date, the first provider name,the first provider address, the first provider phone number, or anycombination thereof, the select portions of the doctor notes associatedwith the first patient being selected based at least in part on thefirst payer set of key words and phrases identified by the first payermachine learning algorithm.

The above summary is not intended to represent each embodiment or everyaspect of the present invention. Additional features and benefits of thepresent invention are apparent from the detailed description and figuresset forth below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a process flow diagram of a method for training one or moremachine learning algorithms according to some implementations of thepresent disclosure;

FIG. 2A is a process flow diagram of a method for prioritizing deniedinsurance claims for appeal to one or more payers according to someimplementations of the present disclosure;

FIG. 2B is an exemplary ordered database of appeal candidate entriesaccording to some implementations of the present disclosure;

FIG. 3 is a process flow diagram of a method for populating an appealdocument for submission to a payer according to some implementations ofthe present disclosure;

FIG. 4 is a schematic representation of the appeal document of FIG. 3according to some implementations of the present disclosure;

FIG. 5 is a block diagram of a network for implementing the method ofFIG. 1 , the method of FIG. 2A, and/or the method of FIG. 3 according tosome implementations of the present disclosure; and

FIG. 6 is a schematic representation of a claim platform systemaccording to some implementations of the present disclosure.

While the disclosure is susceptible to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and will herein be described in detail. Itshould be understood, however, that it is not intended to limit theinvention to the particular forms disclosed, but on the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the invention as defined by theappended claims.

DETAILED DESCRIPTION Overview

Data sources in the healthcare industry are generally output and storedby a variety of systems and in a variety of formats. When a provider(e.g., a healthcare provider, hospital, physician's office, etc.)provides a service to a patient (e.g., an exam, surgery, a diagnostic, atest, treatment, therapy, etc.), the provider often submits an insuranceclaim to a payer (e.g., a health insurance company) for at least partialpayment for the services rendered (or, for services to be rendered inthe future). In some cases, the provider may also submit aprior-authorization request before performing the services. Beforesubmitting a claim to the payer, the provider inputs information from anEHR that contains, for example, procedure codes, diagnosis codes, andother information relating to the rendered service and informationrelating to the patient. The procedure and/or diagnosis codes containedin the electronic health record may be extracted from, for example,doctor notes and/or charts, diagnosis information from exams, lab tests,or notes from radiologists, etc.

The insurance claim or appeal may contain data from a variety ofsources, including data from EHR systems (e.g. doctor notes, diagnosisand service codes, test results, etc.), and data from the provider'srevenue cycle management (“RCM”) that includes medical and billingsoftware, to track patient care episodes from registration toappointment scheduling and payment. The providers submit thisinformation as prior-authorizations, claims and appeals in a variety ofdifferent electronic and paper formats. For instance, some payers allowthese forms to be submitted electronically, but most do not and requirepaper submission. The formats may include structured data fieldsincluding the procedure and diagnosis codes (e.g., CPT codes), data from.pdf or other document types in the form of letters regarding the claimsor appeals, natural language data in the form of text representingdoctor's notes, numerical fields relating the billing and paymenthistory, profile information of payments as structured data and others.

Once this data is submitted to payers, the payers' systems often utilizetheir own algorithms or models to help process the data and determine anoutcome which is usually an acceptance, denial or partial acceptance ofpayment. Payers also layer in some human review, which typicallyincreases with claim complexity and extended claim iterations (back andforth communication with providers, patients, or third-party companies).The payers then submit their decision in a variety of forms back to theprovider, including by letter, through a data portal, and others. Thedecision may also include data in a variety of forms including textbased data from prose relating to a letter, data output from a payerportal in text delimited form, or other formats.

The various data fields that may be output from the RCM, EHR and othersystems may include a variety of structured and unstructured data,natural language text and other data. For instance, the first step in apatient visit is to enter registration data from the visit. For example,this may include:

-   -   Patient profile information (e.g. gender, height, weight,        history of services);    -   Time and date stamp of visit;    -   Chief complaint, which may include natural language or        structured data entered from choices for a specific provider;    -   Doctors notes, which frequently will be in both natural language        and structured data format;    -   Chart notes;    -   Test results, including quantitative structured data and natural        language text from other;    -   Diagnosis/ICD codes;    -   Services/CPT codes;    -   Prior authorization information from payer; and    -   Other information or data.

Next, a claim may be submitted to a payer that may include the priorinformation, and as an example, may also include:

-   -   Dollar amount for services    -   Patient ID or other reference number specific to payer

Then, a payer may respond with an acceptance or denial. As an example,in the case of denial, the payer may submit:

-   -   a quantitative value indicating the amount they will pay; and    -   an adjustment code if there are reasons for the denial.

Then, if the provider appeals the denial, the provider may submit anappeal package that includes any or all of the above data, and includesa letter that describes the reasons for appeal, which will typicallycontain natural language text. After that, the payer will deny or acceptthe appeal (or partially) which will provide more output data that canbe fed into machine learning algorithms.

The likelihood a successful prior-authorization, claim, or appeal maydepend on a variety of factors. For example, different payers (e.g.,insurance companies) may have different algorithms and rules orpractices that will affect the likelihood of a successful appeal. Inother words, a claim that is otherwise identical to a claim successfullyappealed to a first payer may be denied on appeal to a second payer.Similarly, the content of the appeal letter often plays a role in thelikelihood of a successful appeal. For example, certain key words orphrases contained in the appeal letter may aid in increasing thelikelihood that the appeal will be successful. Particularly, key medicalphrases or doctor notes regarding the diagnosis may play a role in anappeal.

Payers generally utilize algorithms for accepting or denyingprior-authorizations, claims, or appeals, and those algorithms arelargely unknown outside the payers, and therefore hard to predict beyondbasic threshold rules that include number of service visits for a typeof service (e.g., six physical therapy sessions for back pain). Thisintroduces tremendous inefficiencies into the system, including back andforth with the provider and payer data sending, etc.

Therefore, machine learning algorithms have been developed based on thedata sources available that can automatically train and input data intothe algorithms to predict the outcomes of the payers' decisions, basedon the documents and other data available. Additionally, systems andmethod have been developed to automatically populate data fieldssubmitted to providers, including letter templates based on structuredand natural language data.

Training Machine Learning Algorithms

Referring to FIG. 1 , a method 100 for training one or more machinelearning algorithms includes a first step 110, a second step 120, anoptional third step 130, a fourth step 140, an optional fifth step 150,and a sixth step 160. The method 100 can be used to train machinelearning algorithms for use in methods of prioritizing denied insuranceclaims for appeal (e.g., method 200 of FIG. 2A) and/or methods ofpopulating fields of an appeal document (e.g., method 300 of FIG. 3 ).

The first step 110 of the method 100 includes receiving raw data from aset of documents or other sources described above. The data may be fromthe sources described above, or other documents that can include, forexample, insurance claim forms prepared by one or more providers,insurance claim appeal forms prepared by one or more providers, doctornotes associated with one or more patients, explanation of benefit formsprepared by one or more payers, claim denial letters prepared by one ormore payers, claim acceptance letters prepared by one or more payers, orany combination thereof. In some implementations, the raw data isreceived in a machine readable format. If required, the first step 110can further include converting text in scanned documents (e.g.,handwritten doctor notes or charts) or electronic documents (e.g.,portable document format files) to machine readable format, using, forexample, optical character recognition.

Generally, machine learning algorithms require training data to identifythe features of interest that they are designed to detect and predict anoutcome. The second step 120 of the method 100 includes selectingtraining data from the raw data received during the first step 110. Allor a portion of the raw data received during the first step 110 can beselected). In some implementations, the second step 120 includesselecting training data from the raw data that is associated with anindividual payer (e.g., one of a plurality of insurance companies). Insuch implementations, as described in further detail below, selectingdata associated with an individual payer results in a trained machinelearning algorithm that is specifically tailored to, for example,predict appeal outcomes for that individual payer.

In some examples, the training data will have a time window of one year,two years, three years, 6 months or other suitable time windows. In someexamples, the machine learning algorithms associated with each payer ina database may be newly trained with a new time window of data. Thiswill ensure that the machine learning algorithms are up to date with thepractices of each payer, which may evolve over time and therefore not bebiased by older data. In other words, the machine learning algorithmsdescribed herein can be updated and optimized with new training data toaccount for new appeal decisions, new claims, etc. associated with eachpayer in the database.

The third step 130 of the method 100 includes labeling sets of thetraining data selected during the second step 120. More specifically,the selected training data is labeled by grouping the selected trainingdata into sets, for example, each set being designated as (1) beingassociated with an insurance prior-authorization, claim, or appeal thathas been accepted by the payer or (2) being associated with an insuranceprior-authorization, claim, or appeal that has been denied by the payer.For example, the selected data can be labeled with designations such as“appeal accepted,” “appeal denied,” “claim paid,” “claim not paid,”“claim paid in full,” “claim paid in part,” etc. As described in furtherdetail below, this labeling of sets of the selected training data aidsin training the machine learning algorithm to predict, for example,whether an insurance claim appeal will be accepted or denied based onnew inputted data.

The optional fourth step 140 of the method 100 includes structuring theselected, labeled training data. This step is optional, and may includepre-processing of data to make it clean for inputting into the machinelearning algorithm. In some examples, data may already be structured andmay be easily input into the algorithms (e.g., CPT and ICD codes).

In other examples, data may take the form of unstructured, naturallanguage text as in the case of an appeal letter, claim letter, claimdenial letter, doctor's notes, etc. Accordingly, the data may be firstprocessed to structure the data using different filtering or processingtechniques, including as an example, utilizing various medicaldictionaries. In other examples, the data from letters may be firststructured by identifying key words and phrases in the text that arerelevant based on the data sources, including words or phrases in denialletters that are associated with healthcare authorizations (e.g., asopposed to focusing on medical terms from medical dictionaries).

Specifically, structuring of the data may refer to identifying andcategorizing each piece of data in each of the labeled sets. Forexample, the data in each labeled set can be categorized into one ormore of a plurality of fields, including, for example, a provider name,a provider address, a provider phone number, a payer name, a payeraddress, a payer phone number, a patient name, a patient address, apatient phone number, a patient social security number or otheridentifier, a patient date of birth, doctor notes associated with thepatient and/or procedure, a procedure code, a diagnosis code, a servicesprovided date, a billed amount, a claim number, an insurance groupnumber, a plan number, a claim denial date, an allowed amount, adeductible amount, a co-pay amount, an adjustment code, a paymentamount, an appeal deadline date, or any combination thereof.

In some implementations, the fourth step 140 includes analyzing thelabeled, selected data to identify a master set of key words and/orphrases used in the labeled, selected data. For example, the master setof key words or phrases can be associated with approved appeals and/ordenied appeals. Because there may be numerous words and phrases in thedata that are generally not correlated with the appeal outcome,selecting a master set of key words and/or phrases from the data aidsthe machine learning algorithm in identifying patterns and successfullypredicting outcomes.

The master set of key words and phrases can be extracted from, forexample, insurance claim forms prepared by one or more providers,insurance claim appeal forms prepared by one or more providers, doctornotes associated with one or more patients, explanation of benefit formsprepared by the payer, claim denial letters prepared by the payer, claimacceptance letters prepared by one or more payers, or any combinationthereof.

To aid in identifying the master set of key words and/or phrases, thefourth step 140 can include comparing words and phrases in the labeled,selected data to words and phrases contained a medical library (e.g., amedical dictionary, medical guidelines, etc.) For example, if the phrase“medial collateral ligament” appears within both the selected data(e.g., doctor's notes) and the medical library, the phrase will beidentified as part of the master set of key words and/or phrases.

In some implementations, the method 100 does not include the optionalfourth step 140. Rather, in such implementations the selected trainingdata is inputted into the machine learning algorithm during the fourthstep 140 without categorizing the data into one or more of a pluralityof fields. This type of machine learning where the training data is notcategorized is referred to as “unstructured learning.” In otherimplementations, the method 100 includes using a combination ofstructured learning and unstructured learning (e.g., a first portion ofthe selected data is categorized into one or more of a plurality offields and a second portion of the selected data is not categorized).

The fifth step 150 of the method 100 includes inputting the labeled,structured training data from step 140 into a machine learningalgorithm, such as, for example, decision trees (“DT”), Bayesiannetworks (“BN”), artificial neural network (“ANN”), support vectormachines (“SVM”), deep learning algorithms, or any combination thereof.

Decision trees (“DT”) are advantageous because of their simplicity andease of understanding. DT are classification graphs that match inputdata to questions asked at each consecutive step in a decision tree. TheDT program moves down the “branches” of the tree based on the answers tothe questions (e.g., the first branch asks: does the payer cover aparticular procedure code? returns yes or no. The second branch asks:does payer cover the billed amount for the procedure code? returns yesor no, etc.)

In one example, a first branch of the DT program asks whether the payercovers a given procedure code, and if the answer is yes, a second branchasks whether the key words or phrases identified during the fourth stepare associated with the procedure code based on its training. If theanswer to the second branch is no, the third branch of the DT program,which asks whether an appeal will be accepted, may return a no answerbecause according to the algorithm's training, this mismatchedcombination of key words or phrases and the procedure code is notassociated with successful appeals. The DT program in this example caninclude another branch which asks whether the billed amount is less thana certain value that the algorithm has been trained to recognize as theupper limit of successful appeals. In this manner, the trained DTalgorithm can predict appeal outcomes.

Bayesian networks (“BN”) are based on the likelihood something is truedepending on given independent variables and are modeled based onprobabilistic relationships. BN are based purely on probabilisticrelationships that determine the likelihood of one variable based onanother or others. For example, BN can model the relationships betweenkey words and phrases and appeal outcomes. Particularly, if certain keywords or phrases are known, a BN can be used to compute the percentagelikelihood that an appeal of a denied claim will be successful. Thus,using an efficient BN algorithm, an inference can be made based on theinput data. BN algorithms are commonly used by the medical domain torepresent reasoning under uncertain conditions for a wide range ofapplications, including disease diagnostics, genetic counseling, andemergency medical decision support system (“MDSS”) design.

Artificial neural networks (“ANN”) are computational models inspired byan animal's central nervous system and map inputs to outputs through anetwork of nodes. However, unlike BN, in ANN, the nodes do notnecessarily represent any actual variable. Accordingly, ANN may have ahidden layer of nodes that are not represented by a known variable to anobserver. ANN's are capable of pattern recognition and have been usedfor the medical and diagnostics fields. Their computing methods make iteasier to understand a complex and unclear process that might go onduring diagnosis of an illness based on a variety of input dataincluding symptoms.

Support vector machines (“SVM”) came about from a framework utilizing ofmachine learning statistics and vector spaces (a linear algebra conceptthat signifies the number of dimensions in linear space) equipped withsome kind of limit-related structure. In some cases, SVMs may determinea new coordinate system that easily separates inputs into twoclassifications. For example, a SVM could identify a line that separatestwo sets of points originating from different classifications of events.SVMs have been applied practically and are theoretically well-founded,but can sometimes be difficult to understand. SVMs have been applied toa number of biological domains, such as MDSS for the diagnosis oftuberculosis infection, tumor classification, and biomarker discovery.

Another type of machine learning algorithm capable of modeling verycomplex relationships that have a lot of variation are deep neuralnetworks (“DNN”). In the IT industry fields, various architectures ofDNN have been proposed to tackle the problems associated with algorithmssuch as ANN by many researchers during the last few decades. These typesof DNN are CNN (Convolutional Neural Network), RBM (Restricted BoltzmannMachine), LSTM (Long Short Term Memory) etc. They are all based on thetheory of ANN. They demonstrate a better performance by overcoming theback-propagation error diminishing problem associated with ANN.

While some examples of machine learning algorithms are described herein,it should be understood that any suitable machine learning algorithms ortechniques (e.g., supervised machine learning algorithms/techniques,unsupervised algorithms/techniques, or any combination thereof) can beused within any step of the method 100.

The optional sixth step 160 of the method 100 includes selectingvalidation data from the raw data received during the first step 110(e.g., a portion of the raw data that is different than the selectedtraining data from the second step 120). The validation data can belabeled and structured in the same or similar manner as the selectedtraining data during the third step 130 and the fourth step 140described above. The validation data can then be inputted into themachine learning algorithm (fifth step 150) to test and validate themachine learning model. For example, validation data associated withgranted appeals and/or denied appeals can be labeled as such,structured, and input into the machine learning algorithm to determinewhether the machine learning algorithm is sufficiently trained topredict appeal outcome (e.g., the machine learning algorithm predictsthe correct outcome more than 80% of the time, the machine learningalgorithm predicts the correct outcome more than 90% of the time,predicts the correct outcome more than 99% of the time, etc.) In thismanner, the trained machine learning algorithm can be validated beforebeing implemented into another method, such as, for example, the methods200, 300 described in further detail below.

The seventh step 170 includes obtaining a trained machine learningalgorithm. As described in further detail below, the trained machinelearning algorithm can be trained for a variety of applications. Forexample, the machine learning algorithm can be trained to predictinsurance claim submission outcomes, the machine learning algorithms canbe trained to determine a percentage likelihood that an insurance claimsubmission will result in a paid claim, the machine learning algorithmcan be trained to identify key words and/or phrases for improving thepercentage likelihood of an insurance claim submission resulting in apaid claim, the machine learning algorithm can be trained to predictinsurance claim appeal outcomes, the machine learning algorithm can betrained to determine a percentage likelihood of a successful appealresulting in a paid claim, the machine learning algorithm can be trainedto predict a monetary value of a paid claim, the machine learningalgorithm can be trained to identify key words and/or phrases forpopulating an appeal document, etc.

In some implementations, the method 100 can be repeated one or moretimes to obtain a plurality of machine learning algorithms where each ofthe plurality of machine learning algorithms is associated with adifferent payer (e.g., a first machine learning algorithm is associatedwith a first insurance company and a second machine learning algorithmis associated with a second insurance company). In such implementations,the second step 120 is repeated and includes selecting training dataassociated with a second payer that is different than the first payerdescribed above in the first iteration of the method 100. Then, thethird step 130 through the seventh step 170 are repeated using thisselected training data to obtain a second trained machine learningalgorithm that is different than the machine learning algorithm obtainedduring the first iteration of the method 100.

Predicting Outcomes of Prior Authorizations, Claims, and Appeals

Referring to FIG. 2A, disclosed is an example method 200 forprioritizing denied insurance claims for appeal to one or more payersincluding a first step 210, a second step 220, a third step 230, afourth step 240, a fifth step 250, a sixth step 260, a seventh step 270,and an optional eighth step 280. In some examples, this methodologycould be applied to prior authorizations and claims. In this example,the method 200 is used to predict the outcome of an appeal of a deniedinsurance claim, the percentage likelihood of an appeal of a deniedinsurance claim being successful, and/or the value of an appeal of adenied insurance claim using a machine learning algorithm (e.g., amachine learning algorithm trained using the method 100 describedabove). Using these predictions/determinations, the method 200 is usedto prioritize denied claims for the provider to appeal and aid ineffectively allocating the provider's resources.

The first step 210 of the method 200 includes receiving a set of rawdata associated with a denied insurance claim. When a payer denies aninsurance claim submitted by a provider, the provider receives a denialcommunication (e.g., a letter, an e-mail, etc.) from the payercontaining data relevant to the claim denial (e.g., an adjustment code,etc.) In cases where the denial communication is a letter from the payer(e.g., as opposed to a communication in an electronic medium), the firststep 210 can include converting the text of the denial communicationinto a machine-readable format using, for example, optical characterrecognition. During the first step 210, the data in the denialcommunication is received by and stored in a memory device of a system(e.g., a computer, a server, a tablet, a smartphone, etc.)

In some implementations, receiving the set of raw data during the firststep 210 includes receiving voice data reproducible as a voice of ahuman (e.g., a doctor, a patient, a nurse, a provider employee, etc., orany combination thereof). For example, the voice data can include voicedata from a doctor (e.g., the doctor's dictations of notes after aservice instance), or the voice data can include voice data from adoctor, a patient, and/or other medical providers (e.g., the voice datarepresents all audio during the entire service instance and/or during aportion of the service instance/doctor visit). The voice data can bereceived from one or more microphones (e.g., for real-time processing)or from an audio file. In such implementations, the first step 210 caninclude processing/converting the voice data into a machine-readableformat.

The second step 220 of the method 200 is similar to fourth step 140 ofthe method 100 described above in that the second step 220 includesstructuring the received set of raw data into a plurality of datasubsets, which can include, for example, a subset of data associatedwith the provider, a subset of data associated with the payer, a subsetof data associated with the patient, a subset of data associated withthe insurance claim, or any combination thereof. In other words, thedata in the set of raw data is categorized into one or more of aplurality of fields.

The subset of data associated with the provider can include, forexample, the provider name, the provider address, the provider phonenumber, or any combination thereof. The subset of data associated withthe payer can include, for example, the payer name, the payer address,the payer phone number, or any combination thereof. The subset of dataassociated with the patient can include, for example, a patient name, apatient address, a patient phone number, a patient social securitynumber or other identifier, a patient date of birth, or any combinationthereof. The subset of data associated with the denied insurance claimcan include, for example, doctor notes associated with the patientand/or procedure, a procedure code, a diagnosis code, a servicesprovided date, a billed amount, a claim number, an insurance groupnumber, a plan number, a claim denial date, an allowed amount, adeductible amount, a copay amount, an adjustment code, a payment amount,an appeal deadline date, or any combination thereof.

The third step 230 includes selecting one of a plurality of machinelearning algorithms. Each of the plurality of machine learningalgorithms can be stored in a memory device of a system (e.g., acomputer, a server, a tablet, a smartphone, etc.) Each of the pluralityof machine learning algorithms can be trained using the method 100described above (FIG. 1 ) and is associated with a different payer(e.g., a first machine learning algorithm is associated with a firstinsurance company and a second machine learning algorithm is associatedwith a second insurance company). Using the structured data subsets fromthe second step 220, which includes a data subset associated with thepayer (e.g., the payer name), the corresponding machine learningalgorithm associated with the payer is selected from the plurality ofmachine learning algorithms.

The fourth step 240 includes inputting the structured data subsets fromthe second step 220 into the machine learning algorithm selected duringthe third step 230. For example, the data subset associated with theclaim denial is inputted into the selected machine learning algorithm.With the data subset(s) inputted into the selected machine learningalgorithm, the fourth step 240 includes a first substep 242 thatpredicts an outcome of an appeal of the denied claim to the payer. Forexample, the selected machine learning algorithm predicts whether anappeal will be successful (e.g., the machine learning algorithm returnsa “yes” value) or whether the appeal will not be successful (e.g., themachine learning algorithm returns a “no” value). As described above,the selected machine learning algorithm is trained to predict the appealoutcome using the method 100 described above.

To illustrate by way of an example, one of the subsets of data from thesecond step 220 contains a procedure code associated with knee surgeryand another one of the subsets of data from the second step 220 containswords from doctor notes that do not contain the word “knee” or“surgery,” but instead include words associated with another procedure(e.g., the procedure code was mistakenly entered). In this example, themachine learning algorithm (which has been trained using the method 100)may predict that an appeal of the denied claim will be denied becausethe procedure code does not match the procedure described in the doctornotes. In another example, one of the subsets of data from the secondstep 220 contains a diagnosis code associated with influenza another oneof the subsets of data from the second step 220 contains a billed amountwhich is $100,000. In this example, the machine learning algorithm maypredict that an appeal of this denied claim will be denied because thebilled amount is much higher than (e.g., 1000 percent higher) the billedamount in successful appeals using this diagnosis code.

Alternatively, in some implementations, the fourth step 240 includes asecond substep 244 which includes determining a percentage likelihoodthat an appeal of the insurance claim to the payer will result in a paidclaim. For example, the machine learning algorithm may determine thatthere is a 0 percent likelihood that the appeal will result in a paidclaim, a 10 percent likelihood that the appeal will result in a paidclaim, a 30 percent likelihood that the appeal will result in a paidclaim, a 50 percent likelihood that the appeal will result in a paidclaim, a 70 percent likelihood that the appeal will result in a paidclaim, a 90 percent likelihood that the appeal will result in a paidclaim, a 100 percent likelihood that the appeal will result in a paidclaim, etc.

In some implementations, after determining the percentage likelihoodduring the second substep 244, the method 200 further includesadjusting, based on the determined likelihood the requested paymentamount. For example, if the second substep 244 determined a 10%likelihood of payment for a requested payment amount of $10,000, therequested payment amount can be adjusted to $5,000, for example. Theadjusted requested payment amount is then inputted into the selectedmachine learning algorithm associated with the payer and the secondsubstep 244 is repeated to determine a second percentage likelihood thatan appeal will result in a paid claim. For example, using the adjustedrequested payment amount, it may be determined that there is a 60%likelihood of payment for the adjusted requested payment amount. In thismanner, the second substep 244 can be repeated one or more times and therequested payment amount can be adjusted until the second substep 244returns a percentage likelihood that is greater than or equal to apredetermined percentage (e.g., over a 50% likelihood that the appealwill result in a paid claim, over a 90% likelihood that the appeal willresult in a paid claim, etc.)

In some implementations, the fourth step 240 includes an optional thirdsubstep 246 that occurs after the first substep 242 or the secondsubstep 244. The optional third substep 246 includes predicting thevalue of a paid claim from an appeal. For example, the machine learningalgorithm can predict the actual value of the paid claim will be apercentage of the billed amount during the third substep 246 (e.g.,$100, $1,000, $10,000, $100,000 etc.) Alternatively, in someimplementations, the machine learning algorithm may predict the value ofthe paid claim as a percentage of the billed amount during the thirdsubstep 246 (e.g., 10 percent of the billed amount, 30 percent of thebilled amount, 50 percent of the billed amount, 70 percent of the billedamount, 90 percent of the billed amount, 100 percent of the billedamount, etc.)

The fifth step 250 includes inputting the output of the fourth step 140and one or more of the structured subsets of data from the second step220 into a database as an appeal candidate entry. The database is storedon a memory device of a system and can be displayed on a display device.As described in further detail below, the entries in the database can beordered to prioritize insurance claim appeal candidates according to oneor more variables.

The sixth step 260 includes repeating, one or more times, each of thefirst step 210, the second step 220, the third step 230, the fourth step240 (including the first substep 242, the second substep 244, the thirdsubstep 246, or any combination thereof), and the fifth step 250. Forexample, a second set of data associated with a second denied insuranceclaim is received during the first step 210. The received second set ofdata is then structured during the second step 220. In someimplementations, the second denied insurance claim was denied by asecond payer that is different that the payer described above (e.g., adifferent insurance company). Thus, during the third step 230, a secondone of the plurality of machine learning algorithms is selected that isthe different than the selected one of the plurality of machine learningalgorithms described above. The fourth step 140 includes inputting thestructured data subsets from the second step 220 into the second machinelearning algorithm. During the fifth step 250, the output of the secondmachine learning algorithm during the fourth step 240 and one or more ofthe structured second subsets of data from the second step 220 are inputinto the database as a second appeal candidate entry. In this manner, aplurality of appeal candidate entries can be added to the database byrepeating the first step 210 through the fifth step 250 one or moretimes.

The seventh step 270 includes ordering the appeal candidate entries inthe database such that appeal candidate entries that are either (1)entries predicted to result in paid claim on appeal or entriesdetermined to have a high percentage likelihood of resulting in a paidclaim on appeal; and/or (2) entries predicted to have a high claim valueare prioritized. The appeal candidate entries can also be ordered orprioritized during the seventh step 270 based on the appeal deadline(e.g., such that a first appeal candidate entry with closer appealdeadline is ordered ahead of a second appeal candidate entry having anappeal deadline that is further away).

As described above, during the first substep 242 of the fourth step 240,the machine learning algorithm predicts that the appeal will result in apaid claim (e.g., the machine learning algorithm outputs “yes” or “no”).In such implementations, the seventh step 270 can include ordering theappeal candidate entries that are predicted to result in a paid claim indescending order according to the value of the paid claim predictedduring the optional third substep 246. That is, the appeal candidateentries can be prioritized based on an estimated revenue that can berecovered. For example, a first appeal candidate with a predicted valueof $10,000 would be ordered ahead of a second appeal candidate entrywith a predicted value of $1,000). In this manner, the ordering aids theprovider in allocating its appeal resources to appeal candidate entriesthat are most likely to result in a larger collection of funds from thepayer.

As described above, using the alternative second substep 244, the fourthstep 240 can determine a percentage likelihood that an appeal of theclaim to the payer will result in a paid claim. In such implementations,the ordering during the seventh step 270 can include ordering theplurality of appeal candidate entries in descending order according tothe determined percentage likelihood. For example, a first appealcandidate entry with a percentage likelihood of 95 percent would beordered ahead of a second appeal candidate entry with a percentagelikelihood of 50 percent. In other implementations, the ordering duringthe seventh step 270 can further include ordering the plurality ofappeal candidate entries according to the determined percentagelikelihood and the predicted value using an algorithm. For example, afirst appeal candidate entry with a percentage likelihood of 80 percentwith a predicted value of $10,000 would be ordered ahead of a secondappeal candidate entry with a percentage likelihood of 95 percent and apredicted value of $100.

In some implementations, the method 200 includes an optional eighth step280 that includes displaying on a display device of a system (e.g., acomputer screen, a laptop screen, a tablet screen, a smartphone screen,a television) the ordered database of appeal candidate entries.Referring to FIG. 2B, an exemplary display 282 including an exemplaryordered database of appeal candidate entries 284A-284C is shown. Avariety of parameters can be displayed for each of the appeal candidateentries 284A-284C, such as, for example, a claim number, a claim amount,a predicted value 286, payer information, an appeal deadline 288, patentinformation, agent information, or any combination thereof. As shown,the appeal candidate entries 284A-284C are ordered in descending orderbased on the predicted value 286. Alternatively, the appeal candidateentries 284A-284C can be ordered based on the appeal deadline 288, suchthat, for example, those entries with the most immediate deadlinesappear first in the ordered list. While three appeal candidate entries284A-284C are shown in FIG. 2B, any suitable number of appeal candidateentries can be displayed sequentially or simultaneously (e.g., one, two,ten, twenty, fifty, etc.) Moreover, the displayed ordered database 282can include a search bar that permits a user to search for a desiredappeal candidate entry using keywords or phrases for any of theparameters described herein. The user can also sort the displayedordered appeal database 282 by the payer and/or a date range (e.g.,days, months, years). Further, the user can export the displayed orderedappeal database 282 to another application or program (e.g., as aspreadsheet). Alternatively, the eighth step 280 can include printingout all or a portion of the ordered database of appeal candidateentries.

The optional eighth step 280 can also include updating the ordereddatabase of appeal candidate entries in real-time based on newlyinputted data. For example, a first appeal candidate that is rankedfirst in the ordered database may be moved down based on a second appealcandidate that the method 200 ranks higher than the first appealcandidate. In another example, based on newly inputted data, thepercentage likelihood of a given appeal candidate in the ordereddatabase may change and the optional eighth step 280 can includereordering the ordered database in real-time as percentage likelihoodsfluctuate given the newest data.

While the method 200 has been described above as being used to predictthe outcome of an appeal of a denied insurance claim or to determine apercentage likelihood that an appeal will result in a paid claim, forexample, methods that are the same as, or similar to, the method 200 arecontemplated herein for use more generally to obtain the same, orsimilar, outputs for insurance claims to be submitted to a payer and/orfor prior-authorizations (e.g., as opposed to an appeal of a deniedclaim). In this manner, the method 200 can be used to predict outcomesof insurance claims at any stage in the life of an insurance claim(e.g., before a claim is first submitted to a payer, before or after aservice is provided, after the claim is submitted to the payer butbefore the payer makes a decision, after a claim is denied but beforethe denied claim is appealed, after a denied claim has been appealed,etc.)

For example, in some implementations, a method that is the similar tothe method 200 can be used to predict an outcome of aprior-authorization, a claim, or an appeal of a denied claim. In suchimplementations, the method includes a first step that is similar to thefirst step 210 described above, which includes receiving (e.g., at aserver) at least a natural language data file representing doctors notesfrom a provider visit related to a service instance. Method alsoincludes a second step that is similar to the second step 220 describedabove, which includes receiving (e.g., at the server), a structured dataset including patient profile data, diagnosis and procedure codes, andquantitative data related to a payment requested. The method includes athird step that is similar to the first substep 242 of the fourth step240 described above, which includes processing at least the naturallanguage data by an unsupervised machine learning algorithm, anddetermining, based on the output of the unsupervised machine learningalgorithm and the structured data a predicted outcome of the paymentrequested. The method also includes outputting an indication of thepredicted outcome of the payment requested.

Populating an Appeal Document

Referring to FIG. 3 , disclosed is an example of a method 300 ofpopulating an appeal document (e.g., the appeal document 400 shown inFIG. 4 ) for submission to a payer or a third party and includes a firststep 310, a second step 320, a third step 330, a fourth step 340, afifth step 350, and a sixth step 360.

The first step 310 is the same as or similar to the first step 210 ofthe method 200 described above in that the first step 310 includesreceiving a set of raw data associated with a denied insurance claim.During the first step 310, the raw data in the denial communication isreceived by and stored in a memory device of a system (e.g., a computer,a server, a tablet, a smartphone, etc.) as machine readable code.

The second step 320 is the same as, or similar to, the second step 220of the method 200 described above and includes structuring the receivedset of raw data into a plurality of data subsets, which can include, forexample, a subset of data associated with the provider, a subset of dataassociated with the payer, a subset of data associated with the patient,a subset of data associated with the insurance claim, or any combinationthereof.

The subset of data associated with the provider can include, forexample, the provider name, the provider address, the provider phonenumber, or any combination thereof. The subset of data associated withthe payer can include, for example, the payer name, the payer address,the payer phone number, or any combination thereof. The subset of dataassociated with the patient can include, for example, a patient name, apatient address, a patient phone number, a patient social securitynumber or other identifier, a patient date of birth, or any combinationthereof. The subset of data associated with the denied insurance claimcan include, for example, doctor notes associated with the patientand/or procedure, a procedure code, a diagnosis code, a servicesprovided date, a billed amount, a claim number, an insurance groupnumber, a plan number, a claim denial date, an allowed amount, adeductible amount, a copay amount, an adjustment code, a payment amount,an appeal deadline date, or any combination thereof.

The third step 330 includes selecting one of a plurality of trainedmachine learning algorithms. Each of the plurality of machine learningalgorithms is stored as machine executable code in a memory device of asystem (e.g., a computer, a server, a tablet, a smartphone, etc.) Eachof the plurality of machine learning algorithms can be trained using themethod 100 described above (FIG. 1 ) and is associated with a differentpayer (e.g., a first machine learning algorithm is associated with afirst insurance company and a second machine learning algorithm isassociated with a second insurance company). Using the structured datasubsets from the second step 320, which includes a data subsetassociated with the payer, the machine learning algorithm associatedwith the payer is selected from the plurality of machine learningalgorithms stored in the memory device.

The fourth step 340 includes inputting all or a portion of thestructured data set into the trained machine learning algorithm selectedduring the third step 330. The fifth step 350 includes identifying,using the selected, trained machine learning algorithm, a set of keywords and phrases to be used in populating an appeal document (e.g., theappeal document 400 of FIG. 4 ). More specifically, the identified setof key words and phrases are identified by the trained machine learningalgorithm as being associated with approved appeals and thus aid inincreases the likelihood of the populated appeal document being approvedby the payer.

For example, one of the inputted subsets of data includes a procedurecode associated with MRI imaging and another one of the inputted subsetsof data includes an adjustment code that is associated with a decisionby the payer that the procedure was not medically necessary. In thisexample, the trained machine learning algorithm can identify key sets ofwords and phrases from another one of the inputted subsets of data, thedoctor notes, such as “medical collateral ligament tear,” which themachine learning algorithm has been trained to associated with acceptedappeals for the payer. Thus, in this example, the set of key words andphrases outputted from the machine learning algorithm are populated inan appeal document during the sixth step 360 described below and aid inincreasing the likelihood that the appeal document will be accepted bythe payer.

The sixth step 360 includes populating fields of an appeal document withappeal data, including the set of key words and phrases identifiedduring the fifth step 350. Referring to FIG. 4 the appeal document 400includes a date field 410, a payer data field 420, a claim data field430, and an appeal content field 440. As described above, the inputted,structured data can include an appeal deadline, and this appeal deadlineis populated in the date field 410 of the appeal document 400. Asdescribed above, the inputted, structured data can include informationassociated with the payer, such as, for example, the payer name,address, phone number, or other identifying information. This inputted,structured information is populated in the payer data field 420 of theappeal document 400. Similarly, as described above, the trained machinelearning algorithm identifies a set of key words and phrases that areassociated with granted appeals, and this set of key words and phrasesis populated in the appeal content field 440 of the appeal document oras suggested text phrases that may optionally be input.

The identified key words and phrases that are populated in the appealcontent field 440 can be populated in the form of full sentences orbullet points which a user can then use in drafting full sentences tocomplete the appeal document 400. Further, it should be understood thatthe appeal document 400 can more generally include more or less fieldsrelevant to an appeal of a denied insurance claim that can be populatedusing the method 300 described above.

In some implementations, the method 300 can further include displayingthe populated appeal document on a display device, printing out all or aportion of the populated appeal document, and/or sending the populatedappeal document to the payer in an electronic medium for a decision.

In some implementations, the identified key words or phrases from step350 can be communicate to a provider (e.g., a doctor's office) ratherthan, or in addition to, being populated in the appeal document 400. Insuch implementations, the provider (e.g., doctor) can use the identifiedkey words or phrases during an appointment with a patient so that thekey words or phrases will be included in the doctor's notes. Asdescribed herein, the language in the doctor's notes is often animportant factor in whether a claim is granted or denied in the firstinstance and/or on appeal. Thus, inserting these key words or phrases atthe outset in the initial doctor's notes can aid in increasing thelikelihood that a claim will be paid by the payer upon the initialsubmission.

In some implementations, the methods 100, 200, and 300 described abovecan be used conjunctively. For example, in such implementations, themethod 100 can be used to train a machine learning algorithm to predictoutcomes of insurance appeal claims submitted to a first payer and/orpredict the value of the paid claim from a successful appeal. Using thetrained machine learning algorithm obtained from the method 100, themethod 200 is then used to obtain an ordered database of appealcandidate entries based on (i) the predicted outcome of the appealaccording to the trained machine learning algorithm, (ii) the predictedvalue of the paid claim from a successful appeal according to thetrained machine learning algorithm, or (iii) both. Using the method 300fields of an appeal document are populated for each of the appealcandidate entries in the ordered database, with the first appealcandidate entry in the ordered database being first, etc. In thismanner, the methods 100, 200, and 300 can be used together in someimplementations to both prioritize which denied insurance claims toappeal and to populate fields of appeal documents according to theprioritization.

Referring to FIG. 5 , the method 100, the method 200, and/or the method300 described herein can be implemented with a network 500 that includesa provider system 510, a payer system 520, a server 530, and a cloudsystem 540. Each of the provider system 510, the payer system 520, theserver 530, and the cloud system 540 includes a memory device forstoring machine executable instructions and one or more processors forexecuting the machine executable instructions. The memory device of theprovider system 510, the payer 520 system, the server 530, and the cloudsystem 540, or any combination thereof can be configured to communicatewith one another (e.g., send and receive data).

Any of the trained or untrained machine learning algorithms describedherein can be stored in the memory device of the provider system 510,the server 530, and the cloud system 540, or any combination thereof.The training of the machine learning algorithms described during themethod 100 can be implemented using the one or more processors of thememory device of the provider system 510, the server 530, and the cloudsystem 540, or any combination thereof. Likewise, any of the data setsdescribed herein (e.g., the raw data set received during the first step210 of the method 200, the structured data set from the second step 220of the method 200, etc.), the ordered database described in reference tothe seventh step 270 of the method 200, the appeal document 400, etc.,can be stored in the memory device of the provider system 510, theserver 530, and the cloud system 540, or any combination thereof. Moregenerally, any of the steps of the methods 100, 200, and 300 can beimplemented using any one of the provider system 510, the server 530,and the cloud system 540, or any combination thereof.

Referring to FIG. 6 , a claim platform system 600 includes a centralserver 610, a provider server 620, a payer server 630, and a patientdevice 640. Each of the central server 610, the provider server 620, thepayer server 630, and the patient device 640 includes a memory devicefor storing machine executable instructions and one or more processorsfor executing the machine executable instructions. The provider server620, the payer server 630, and the patient device 640 are eachcommunicatively coupled to the central server 610 such that data can betransmitted between the central server 610 and each of the providerserver 620, the payer server 630, and the patient device 640. Thecentral server 610 includes a central claim module 660, a providermodule 662, a payer module 664, a patient module 666, and a claimsettlement module 668. Each of the modules can be implemented usingmethods that are the same, or similar to, the methods 100, 200, 300 (orany combination thereof) described above.

The central claim module 660 is stored on the central server 610 and canbe accessed by the provider server 620, the payer server 630, thepatient device 640, or any combination thereof. The central claim module660 includes a set of data associated with one or more insurance claimspermits all parties (e.g., the provider, the payer, and/or the patient)to view the same information associated with the one or more insuranceclaims. For example, each of the provider, the payer, and the patientcan view the requested payment amount associated with an insurance claimthrough the claim module 660.

The provider module 662 is stored on the central server 610 and can beaccessed by the provider server 620. In some implementations, theprovider module 662 can display a dashboard on a provider device (e.g.,computer) that is connected to the provider server 620. The providermodule 662 can further include a notification module, a workflow module,an analytics module, or any combination thereof that populate thedashboard on the provider device. For example, the notification modulecan be configured to transmit a notification to the provider server 620that alerts the provider, via the displayed dashboard, regarding thestatus of an insurance claim (e.g., a payer has denied a claim). Theanalytics module can be configured to determine, for example, a claimdenial percentage for a given payer, and display that percentage via thedashboard.

The provider module 662 can be configured to, for example, predict theoutcome of an insurance claim submitted by the provider to a payer,determine the percentage likelihood that an insurance claim submitted bythe provider will result in a paid claim, determine the percentagelikelihood that an appeal of a denied insurance claim will result in apaid claim, etc., using methods that are the same as, or similar to, themethods 100, 200, and/or 300 described above. Using the workflow module,a prioritized list of denied claims to be submitted for appeal can bedisplayed on the dashboard through the provider server 620. In thismanner, the dashboard can include provider specific information, suchas, for example, a prioritized list of insurances claims for submissionto one or more payers, a prioritized list of denied insurance claims tobe appealed to one or more payers, etc. The provider module 662 can alsoreceive data associated with a new insurance claim (or appeal) from theprovider server 620 (e.g., the provider inputs the data manually orautomatically through the dashboard displayed on a provider device, andthis inputted data is transmitted to the central server 610 via theprovider server 620).

The payer module 664 is stored on the central server 610 and can beaccessed by the provider server 620. In some implementations, the payermodule 664 displays a dashboard on a provider device (e.g., computer)that is connected to the provider server 630. The payer module 664 canfurther include a notification module, a workflow module, an analyticsmodule, or any combination thereof that populate the dashboard on thepayer device. The dashboard can include, for example, a list ofinsurance claims submitted by one or more providers, a list of insuranceclaim appeals submitted by one or more providers, etc. The payer module664 can also receive payer data associated with an insurance claim (orappeal) from the provider server 630 (e.g., that the payer is denyingthe claim or appeal).

The patient module 666 is stored on the central server 610 and can beaccessed by the patient device 640. In some implementations, the patientmodule 666 displays a dashboard on the patent device 640 (e.g., asmartphone). The patient module 666 can further include a notificationmodule, a workflow module, an analytics module, or any combinationthereof that populate the dashboard on the payer device. The dashboarddisplayed on the patient device 640 can include, for example, a list ofinsurance claims submitted by a provider on behalf of the patient, alist of insurance claims submitted by the patient, etc.

The claim settlement module 668 that can be accessed by the providerserver 620, the payer server 630, the patient device 640, or anycombination thereof. The claim settlement module 668 permits each partyto settle one or more pending insurance claims at any point in the claimlife cycle (e.g., after the claim has been initially submitted by aprovider, during an appeal of a denied insurance claim, etc.) In someimplementations, the claim settlement module 668 permits the providerand/or the payer to input one or more rules for settling one or moreclaims at any point during the claim lifecycle. For example, using thepayer server 630 to communicate with the central server 610 and theclaim settlement module 668, the payer can input a payment amount thatit is willing to pay for a given insurance claim (e.g., 80% of therequested payment amount). Likewise, using the provider server 620 tocommunicate with the central server 610 and the claim settlement module668, the provider can input a payment amount that is willing to acceptfor the insurance claim (e.g., 80% of the requested payment amount,which is based on the output of a machine learning algorithm configuredto predict that the payer will pay 80% of the requested amount). In thisexample, the claim settlement module 668 can then notify, the provider,the payer, and/or the patient that the claim has been settled through anautomated alert.

It should also be understood that the disclosure herein may be moregenerally implemented with any type of hardware and/or software, and maybe a pre-programmed general purpose computing device. For example, thesystem may be implemented using a server, a personal computer, aportable computer, a thin client, or any suitable device or devices. Thedisclosure and/or components thereof may be a single device at a singlelocation, or multiple devices at a single, or multiple, locations thatare connected together using any appropriate communication protocolsover any communication medium such as electric cable, fiber optic cable,or in a wireless manner.

It should also be noted that the disclosure is illustrated and discussedherein as having a plurality of modules which perform particularfunctions. It should be understood that these modules are merelyschematically illustrated based on their function for clarity purposesonly, and do not necessary represent specific hardware or software. Inthis regard, these modules may be hardware and/or software implementedto substantially perform the particular functions discussed. Moreover,the modules may be combined together within the disclosure, or dividedinto additional modules based on the particular function desired. Thus,the disclosure should not be construed to limit the present invention,but merely be understood to illustrate one example implementationthereof.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data (e.g., an HTML page) to aclient device (e.g., for purposes of displaying data to and receivinguser input from a user interacting with the client device). Datagenerated at the client device (e.g., a result of the user interaction)can be received from the client device at the server.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, e.g., as a data server, or that includes a middlewarecomponent, e.g., an application server, or that includes a front-endcomponent, e.g., a client computer having a graphical user interface ora Web browser through which a user can interact with an implementationof the subject matter described in this specification, or anycombination of one or more such back-end, middleware, or front-endcomponents. The components of the system can be interconnected by anyform or medium of digital data communication, e.g., a communicationnetwork. Examples of communication networks include a local area network(“LAN”) and a wide area network (“WAN”), an inter-network (e.g., theInternet), and peer-to-peer networks (e.g., ad hoc peer-to-peernetworks).

Implementations of the subject matter and the operations described inthis specification can be implemented in digital electronic circuitry,or in computer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Implementations of the subjectmatter described in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus. Alternatively, orin addition, the program instructions can be encoded on anartificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described in this specification can be implemented asoperations performed by a “data processing apparatus” on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

It is to be understood that many modifications and variations may bedevised given the above description of the general principles of thepresent disclosure. It is intended that all such modifications andvariations be considered as within the spirit and scope of the presentdisclosure, as defined in the following claims.

1. A method of prioritizing denied insurance claims for appeal to one ormore payers, the method comprising: training a first machine learningalgorithm to determine a percentage likelihood that an appeal of aninsurance claim to a first payer will result in a paid insurance claim;training a second machine learning algorithm to determine a percentagelikelihood that an appeal of an insurance claim to a second payer willresult in a paid insurance claim; receiving a first set of dataassociated with a first insurance claim denied by the first payer;receiving a second set of data associated with a second insurance claimdenied by the second payer; inputting at least a portion of the firstset of data into the first machine learning algorithm; using the firstmachine learning algorithm, determining a first percentage likelihoodthat an appeal of the first insurance claim to the first payer willresult in a first paid claim; inputting at least a portion of the secondset of data into the second machine learning algorithm; and using thesecond machine learning algorithm, determining a second percentagelikelihood that an appeal of the second insurance claim to the secondpayer will result in a second paid claim. 2.-44. (canceled)